Significance Support Vector Regression for Image Denoising

Abstract

As an extension of the support vector machine, support vector regression (SVR) plays a significant role in image denoising. However, due to ignoring the spatial distribution information of noisy pixels, the conventional SVR denoising model faces the bottleneck of overfitting in the case of serious noise interference, which leads to a degradation of the denoising effect. For this problem, this paper proposes a significance measurement framework for evaluating the sample significance with sample spatial density information. Based on the analysis of the penalty factor in SVR, significance SVR (SSVR) is presented by assigning the sample significance factor to each sample. The refined penalty factor enables SSVR to be less susceptible to outliers in the solution process. This overcomes the drawback that the SVR imposes the same penalty factor for all samples, which leads to the objective function paying too much attention to outliers, resulting in poorer regression results. As an example of the proposed framework applied in image denoising, a cutoff distance-based significance factor is instantiated to estimate the samples’ importance in SSVR. Experiments conducted on three image datasets showed that SSVR demonstrates excellent performance compared to the best-in-class image denoising techniques in terms of a commonly used denoising evaluation index and observed visual.

Point-of-care testing for cerebral edema types based on symmetric cancellation near-field coupling phase shift and support vector machine

“…When tends to infinity, samples with classification errors are not allowed, and the probability of overfitting is very high, making it difficult to deal with individual differences of targets and increase the misdiagnosis rates. When tends to 0, the model no longer focuses on classification accuracy but on larger intervals [ 38 ]. This will affect convergence and lead to increased misdiagnosis rates.…”